System and method for determining an auscultation quality metric

Abstract

A computer-implemented method, a computer system, and a non-transitory computer readable medium are provided that perform a method for determining an auscultation quality metric (AQM). The computer-implemented method includes obtaining an acoustic signal representative of pulmonary sounds from a patient; determining a plurality of derived signals from the acoustic signal; performing a regression analysis on the plurality of derived signals; and determining the AQM from the regression analysis.

What is claimed is: 
     
         1 . A computer-implemented method for determining an auscultation quality metric (AQM), the computer-implemented method comprising:
 obtaining an acoustic signal representative of pulmonary sounds from a patient;   determining a plurality of derived signals from the acoustic signal;   performing a regression analysis on the plurality of derived signals; and   determining the AQM from the regression analysis.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the plurality of derived signals comprise a spectral energy signal, a spectral shape signal, a temporal dynamics signal, a fundamental frequency signal, a mean error signal, a reconstruction error signal, a bandwidth signal, a spectral flatness signal, a spectral irregularity signal, a high modulation rate energy signal, or a low modulation rate energy signal. 
     
     
         3 . The computer-implemented method of  claim 2 , wherein the mean error signal and the reconstruction error signal are obtained from a trained neural network. 
     
     
         4 . The computer-implemented method of  claim 3 , wherein the trained neural network is a trained convolutional autoencoder. 
     
     
         5 . The computer-implemented method of  claim 4 , wherein the trained convolutional autoencoder is a three-layer autoencoder, a four-layer autoencoder, or a five-layer autoencoder. 
     
     
         6 . The computer-implemented method of  claim 1 , further comprising training a convolutional autoencoder from a set of high-quality acoustic signals obtained from a variety of patients. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the AQM ranges from 0 to 1 where 0 represents the lowest quality and 1 represents the highest quality for the acoustic signal that is obtained. 
     
     
         8 . A computer system comprising:
 a hardware processor;   a non-transitory computer readable medium comprising instructions that when executed by the hardware processor perform a method for determining an auscultation quality metric (AQM), comprising:   obtaining an acoustic signal representative of pulmonary sounds from a patient;   determining a plurality of derived signals from the acoustic signal;   performing a regression analysis on the plurality of derived signals; and   determining the AQM from the regression analysis.   
     
     
         9 . The computer system of  claim 8 , wherein the plurality of derived signals comprise a spectral energy signal, a spectral shape signal, a temporal dynamics signal, a fundamental frequency signal, a mean error signal, a reconstruction error signal, a bandwidth signal, a spectral flatness signal, a spectral irregularity signal, a high modulation rate energy signal, or a low modulation rate energy signal. 
     
     
         10 . The computer system of  claim 9 , wherein the mean error signal and the reconstruction error signal are obtained from a trained neural network. 
     
     
         11 . The computer system of  claim 10 , wherein the trained neural network is a trained convolutional autoencoder. 
     
     
         12 . The computer system of  claim 11 , wherein the trained convolutional autoencoder is a three-layer autoencoder, a four-layer autoencoder, or a five-layer autoencoder. 
     
     
         13 . The computer system of  claim 8 , wherein the hardware processor is further configured to execute the method comprising training a convolutional autoencoder from a set of acoustic signal obtained from a variety of patients. 
     
     
         14 . The computer system of  claim 8 , wherein the AQM ranges from 0 to 1 where 0 represents the lowest quality and 1 represents the highest quality for the acoustic signal that is obtained. 
     
     
         15 . A non-transitory computer readable medium comprising instructions that when executed by a hardware processor perform a method for determining an auscultation quality metric (AQM), method comprising:
 obtaining an acoustic signal representative of pulmonary sounds from a patient;   determining a plurality of derived signals from the acoustic signal;   performing a regression analysis on the plurality of derived signals; and   determining the AQM from the regression analysis.   
     
     
         16 . The non-transitory computer readable medium of  claim 15 , wherein the plurality of derived signals comprise a spectral energy signal, a spectral shape signal, a temporal dynamics signal, a fundamental frequency signal, a mean error signal, a reconstruction error signal, a bandwidth signal, a spectral flatness signal, a spectral irregularity signal, a high modulation rate energy signal, or a low modulation rate energy signal. 
     
     
         17 . The non-transitory computer readable medium of  claim 16 , wherein the mean error signal and the reconstruction error signal are obtained from a trained neural network. 
     
     
         18 . The non-transitory computer readable medium of  claim 17 , wherein the trained neural network is a trained convolutional autoencoder. 
     
     
         19 . The non-transitory computer readable medium of  claim 18 , wherein the trained convolutional autoencoder is a three-layer autoencoder, a four-layer autoencoder, or a five-layer autoencoder. 
     
     
         20 . The non-transitory computer readable medium of  claim 15 , wherein the method further comprises training a convolutional autoencoder from a set of acoustic signal obtained from a variety of patients. 
     
     
         21 . The non-transitory computer readable medium of  claim 15 , wherein the AQM ranges from 0 to 1 where 0 represents the lowest quality and 1 represents the highest quality for the acoustic signal that is obtained.